Prediction of mediastinal lymph node metastasis based on 18F-FDG PET/CT imaging using support vector machine in non-small cell lung cancer

Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and poses a serious threat to human health. Due to the advances in lung cancer screening, more and more clinical T1 NSCLC defined as a tumor with a maximum diameter of 3cm surrounded by lung tissue or visceral pleura have been detected and have achieved favorable treatment outcomes, greatly improving the prognosis of NSCLC patients. However, the preoperative lymph node staging and intraoperative lymph node dissection patterns of operable clinical T1 NSCLC are still subject to much disagreement, as well as the heterogeneity between primary tumors and metastatic lymph nodes poses a challenge in designing effective treatment strategies. This article comprehensively describes the clinical risk factors of clinical T1 NSCLC lymph node metastasis, and its invasive and non-invasive prediction, focusing on the genetic heterogeneity between the primary tumor and the metastatic lymph nodes, which is significant for a thoroughly understanding of the biological behavior of early-stage NSCLC.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer

Objective.Vital rules learned from fluorodeoxyglucose positron emission tomography (FDG-PET) radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (gray wolf optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer.Approach.Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (ΔSUVmean ⩾ 20% decline) for classification and a continuous response measure (ΔSUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression.Main results.GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC: 0.58-0.86 vs. 0.52-0.78,p= 0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE: 0.162-0.192) performed better numerically for low-dimensional models (p= 0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE: 0.189-0.219,p< 0.004).Significance. The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics

BACKGROUND

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed.

METHODS

Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort.

RESULTS

Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780).

CONCLUSION

This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

Pretreatment 18F-FDG uptake heterogeneity may predict treatment outcome of combined Trastuzumab and Pertuzumab therapy in patients with metastatic HER2 positive breast cancer

OBJECTIVE

Intra-tumoral heterogeneity of 18F-fluorodeoxyglucose (18F-FDG) uptake has been proven to be a surrogate marker for predicting treatment outcome in various tumors. However, the value of intra-tumoral heterogeneity in metastatic Human epidermal growth factor receptor 2(HER2) positive breast cancer (MHBC) remains unknown. The aim of this study was to evaluate 18F-FDG uptake heterogeneity to predict the treatment outcome of the dual target therapy with Trastuzumab and Pertuzumab(TP) in MHBC.

METHODS

Thirty-two patients with MHBC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) scan before TP were enrolled retrospectively. The region of interesting (ROI) of the lesions were drawn, and maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), metabolic tumor volume (MTV) and heterogeneity index (HI) were recorded. Correlation between PET/CT parameters and the treatment outcome was analyzed by Spearman Rank Test. The ability to predict prognosis were determined by time-dependent survival receiver operating characteristic (ROC) analysis. And the survival analyses were then estimated by Kaplan-Meier method and compared by log-rank test.

RESULTS

The survival analysis showed that HI50% calculated by delineating the lesion with 50%SUVmax as threshold was a significant predictor of patients with MHBC treated by the treatment with TP. Patients with HI50% (≥ 1.571) had a significantly worse prognosis of progression free survival (PFS) (6.87 vs. Not Reach, p = 0.001). The area under curve (AUC), the sensitivity and the specificity were 0.88, 100% and 63.6% for PFS, respectively.

CONCLUSION

18F-FDG uptake heterogeneity may be useful for predicting the prognosis of MHBC patients treated by TP.

Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography

Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.

Predictive value of 18F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation

PURPOSE

To develop and validate the predictive value of an 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) model for breast cancer neoadjuvant chemotherapy (NAC) efficacy based on the tumor-to-liver ratio (TLR) radiomic features and multiple data pre-processing methods.

METHODS

One hundred and ninety-three breast cancer patients from multiple centers were retrospectively included in this study. According to the endpoint of NAC, we divided the patients into pathological complete remission (pCR) and non-pCR groups. All patients underwent 18F-FDG PET/CT imaging before NAC treatment, and CT and PET images volume of interest (VOI) segmentation by manual segmentation and semi-automated absolute threshold segmentation, respectively. Then, feature extraction of VOI was performed with the pyradiomics package. A total of 630 models were created based on the source of radiomic features, the elimination of the batch effect approach, and the discretization method. The differences in data pre-processing approaches were compared and analyzed to identify the best-performing model, which was further tested by the permutation test.

RESULTS

A variety of data pre-processing methods contributed in varying degrees to the improvement of model effects. Among them, TLR radiomic features and Combat and Limma methods that eliminate batch effects could enhance the model prediction overall, and data discretization could be used as a potential method that can further optimize the model. A total of seven excellent models were selected and then based on the AUC of each model in the four test sets and their standard deviations, we selected the optimal model. The optimal model predicted AUC between 0.7 and 0.77 for the four test groups, with p-values less than 0.05 for the permutation test.

CONCLUSION

It is necessary to enhance the predictive effect of the model by eliminating confounding factors through data pre-processing. The model developed in this way is effective in predicting the efficacy of NAC for breast cancer.

Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer

Objective

This study aimed to establish a predictive model for occult lymph node metastasis (LNM) in patients with clinical stage I-A non-small cell lung cancer (NSCLC) based on contrast-enhanced CT.

Methods

A total of 598 patients with stage I-IIA NSCLC from different hospitals were randomized into the training and validation group. The "Radiomics" tool kit of AccuContour software was employed to extract the radiomics features of GTV and CTV from chest-enhanced CT arterial phase pictures. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to reduce the number of variables and develop GTV, CTV, and GTV+CTV models for predicting occult lymph node metastasis (LNM).

Results

Eight optimal radiomics features related to occult LNM were finally identified. The receiver operating characteristic (ROC) curves of the three models showed good predictive effects. The area under the curve (AUC) value of GTV, CTV, and GTV+CTV model in the training group was 0.845, 0.843, and 0.869, respectively. Similarly, the corresponding AUC values in the validation group were 0.821, 0.812, and 0.906. The combined GTV+CTV model exhibited a better predictive performance in the training and validation group by the Delong test (p<0.05). Moreover, the decision curve showed that the combined GTV+CTV predictive model was superior to the GTV or CTV model.

Conclusion

The radiomics prediction models based on GTV and CTV can predict occult LNM in patients with clinical stage I-IIA NSCLC preoperatively, and the combined GTV+CTV model is the optimal strategy for clinical application.

Clinical application of AI-based PET images in oncological patients

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

A Multi-Modal Heterogeneous Graph Forest to Predict Lymph Node Metastasis of Non-Small Cell Lung Cancer

Lymph node metastasis (LNM) is critical for treatment decision-making for cancer patients, but it is difficult to diagnose accurately before surgery. Machine learning can learn nontrivial knowledge from multi-modal data to support accurate diagnosis. In this paper, we proposed a Multi-modal Heterogeneous Graph Forest (MHGF) approach to extract the deep representations of LNM from multi-modal data. Specifically, we first extracted the deep image features from CT images to represent the pathological anatomic extent of the primary tumor (pathological T stage) using a ResNet-Trans network. And then, a heterogeneous graph with six vertices and seven bi-directional relations was defined by medical experts to describe the possible relations between the clinical and image features. After that, we proposed a graph forest approach to construct the sub-graphs by removing each vertex in the complete graph iteratively. Finally, we used graph neural networks to learn the representations of each sub-graph in the forest to predict LNM and averaged all the prediction results as final results. We conducted experiments on 681 patients' multi-modal data. The proposed MHGF achieves the best performances with a 0.806 AUC value and 0.513 AP value compared with state-of-art machine learning and deep learning methods. The results indicate that the graph method can explore the relations between different types of features to learn effective deep representations for LNM prediction. Moreover, we found that the deep image features about the pathological anatomic extent of the primary tumor are useful for LNM prediction. And the graph forest approach can further improve the generalization ability and stability of the LNM prediction model.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method

BACKGROUND

Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically.

PURPOSE

To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method.

METHODS

In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively.

RESULTS

Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use.

CONCLUSION

This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.

Lung Cancer Prediction from Text Datasets Using Machine Learning

Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

Lymph node micrometastasis in non-small cell lung cancer

Lung cancer has some of the highest morbidity and mortality rates of all cancers, and an important risk factor for mortality in patients with lung cancer is tumor metastasis. Even if a tumor is completely removed at an early stage of the disease, quite a number of patients still have the risk of recurrence. With the advent of molecular diagnostic and therapeutics, more and more studies have found that a poor prognosis may be related to lymph node micrometastasis. However, clinicians still find that predicting the prognosis and choosing the type of surgery and postoperative adjuvant chemotherapy are still challenging. Thus, this article reviews the current research status of lymph node micrometastasis in non-small cell lung cancer, envision to provide some updates and insights in this area.

Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer

Background: To develop and validate a radiomics model based on ¹⁸F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative ¹⁸F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative ¹⁸F-FDG PET/CT radiomic features.

Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer

Background

Accurate evaluation of lymph node (LN) status is critical for determining the treatment options in patients with non-small cell lung cancer (NSCLC). This study aimed to develop and validate a ¹⁸F-FDG PET-based radiomic model for the identification of metastatic LNs from the hypermetabolic mediastinal–hilar LNs in NSCLC.

Methods

We retrospectively reviewed 259 patients with hypermetabolic LNs who underwent pretreatment ¹⁸F-FDG PET/CT and were pathologically confirmed as NSCLC from two centers. Two hundred twenty-eight LNs were allocated to a training cohort (LN = 159) and an internal validation cohort (LN = 69) from one center (7:3 ratio), and 60 LNs were enrolled to an external validation cohort from the other. Radiomic features were extracted from LNs of PET images. A PET radiomics signature was constructed by multivariable logistic regression after using the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation. The PET radiomics signature (model 1) and independent predictors from CT image features and clinical data (model 2) were incorporated into a combined model (model 3). A nomogram was plotted for the complex model, and the performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness.

Results

The area under the curve (AUC) values of model 1 were 0.820, 0.785, and 0.808 in the training, internal, and external validation cohorts, respectively, showing good diagnostic efficacy for lymph node metastasis (LNM). Furthermore, model 2 was able to discriminate metastatic LNs in the training (AUC 0.780), internal (AUC 0.794), and external validation cohorts (AUC 0.802), respectively. Model 3 showed optimal diagnostic performance among the three cohorts, with an AUC of 0.874, 0.845, and 0.841, respectively. The nomogram based on the model 3 showed good discrimination and calibration.

Conclusions

Our study revealed that PET radiomics signature, especially when integrated with CT imaging features, showed the ability to identify true and false positives of mediastinal–hilar LNM detected by PET/CT in patients with NSCLC, which would help clinicians to make individual treatment decisions.

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.

Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the ¹⁸F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.

Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).

Conclusions: The RM based on ¹⁸F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.